An algorithm for the properties of the integrated process control with bounded adjustments and EWMA monitoring

An integrated process control (IPC) procedure is a scheme which simultaneously applies the engineering process control (EPC) and statistical process control (SPC) techniques to reduce the variation of a process. The EPC is performed by adjusting the process, which will continually wander away from the target by its inherent disturbances. The SPC is implemented by monitoring the process, which will be changed to an undesirable state by special causes. The wandering behaviour of the process is often well-fitted by an IMA(0,1,1) process and the occurrence of a special cause is considered to change the process level. For adjusting, the bounded adjustment scheme is used and for monitoring the EWMA chart is used. The performance of the IPC procedure is evaluated in terms of the expected cost per unit interval (ECU). In designing the IPC procedure for practical use, it is essential to derive its properties constituting the ECU, but no analytical solution has been known yet. As an alternative, an algorithm for calculation of the properties is derived by using a Markov chain approach when the process is in control and a Monte Carlo simulation when out of control.

[1]  Wei Jiang,et al.  SPC Monitoring of MMSE- and Pi-Controlled Processes , 2002 .

[2]  W. Woodall On the Markov Chain Approach to the Two-Sided CUSUM Procedure , 1984 .

[3]  Frederick W. Faltin,et al.  Statistical Control by Monitoring and Feedback Adjustment , 1999, Technometrics.

[4]  Marion R. Reynolds,et al.  EWMA control charts with variable sample sizes and variable sampling intervals , 2001 .

[5]  Changsoon Park,et al.  Economic Design of a Variable Sampling Rate X-bar Chart , 1999 .

[6]  Douglas C. Montgomery,et al.  SPC with correlated observations for the chemical and process industries , 1995 .

[7]  Harriet Black Nembhard,et al.  INTEGRATED PROCESS CONTROL FOR STARTUP OPERATIONS , 1998 .

[8]  George E. P. Box,et al.  Selection of Sampling Interval and Action Limit for Discrete Feedback Adjustment , 1994 .

[9]  E. Saniga Economic Statistical Control-Chart Designs with an Application to X̄ and R Charts@@@Economic Statistical Control-Chart Designs with an Application to X and R Charts , 1989 .

[10]  Wei Jiang,et al.  An economic model for integrated APC and SPC control charts , 2000 .

[11]  Kwok-Leung Tsui,et al.  A mean-shift pattern study on integration of SPC and APC for process monitoring , 1999 .

[12]  J. Bert Keats,et al.  Combining SPC and EPC in a Hybrid Industry , 1998 .

[13]  Monica Dumitrescu,et al.  Control Charts and Feedback Adjustments for a Jump Disturbance Model , 2000 .

[14]  Lonnie C. Vance,et al.  The Economic Design of Control Charts: A Unified Approach , 1986 .

[15]  George E. P. Box,et al.  Statistical process monitoring and feedback adjustment: a discussion , 1992 .

[16]  S. A. Vander Wiel,et al.  Monitoring processes that wander using integrated moving average models , 1996 .

[17]  Changsoon Park,et al.  Economic design of a variable sampling rate EWMA chart , 2004 .

[18]  Erwin M. Saniga,et al.  Economic Statistical Control-Chart Designs With an Application to and R Charts , 1989 .

[19]  Tim Kramer,et al.  Process Control From An Economic Point of View-Chapter 1: Industrial Process Control , 1990 .

[20]  Douglas C. Montgomery,et al.  Economic Design of T2 Control Charts to Maintain Current Control of a Process , 1972 .

[21]  Wei Jiang A joint monitoring scheme for automatically controlled processes , 2004 .

[22]  D. Montgomery ECONOMIC DESIGN OF AN X CONTROL CHART. , 1982 .